The 2000s saw a shift in AI research towards machine learning, leading to more capable AI systems learning from vast amounts of data. This era also brought about the emergence of organizations like the Machine Intelligence Research Institute (MIRI) and OpenAI, dedicated to ensuring AI systems are safe and beneficial. These institutions work as guardians to prevent AI systems from veering off course and develop frameworks to guide AI development ethically.
As AI systems evolved from rule-based to machine learning, new alignment techniques were needed to ensure complex models remained aligned with human values. One significant advancement in alignment research is the focus on interpretability, making AI models more transparent so researchers can understand their decision-making processes.
Understanding the history and evolution of LLM alignment is crucial to appreciate the complexity and importance of this field. Aligning LLM systems with human values is essential to ensure they enhance rather than harm society. By studying this history, we can better prepare for the future of AI development. In the next part of the series, the focus will be on outer and inner alignment approaches, exploring the methodologies and strategies used to keep AI models on track.
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